An autonomous vehicle is a motorized vehicle that can operate without human conduction. An exemplary autonomous vehicle includes a plurality of sensor systems, such as, but not limited to, a lidar sensor system, a camera sensor system, a radar sensor system, amongst others, wherein the autonomous vehicle operates based upon sensor signals output by the sensor systems. Typically, sensor signals are received by a computing system in communication with the plurality of sensor systems that may capture an object in proximity to the autonomous vehicle. The sensor signals are processed by the computing system to determine if an object has been detected and, based on the determination, the computing system executes instructions to control a mechanical system of the autonomous vehicle, for example, a vehicle propulsion system, a braking system, or a steering system, based upon the determination that the object has been detected.
Conventional navigation of an autonomous vehicle has relied upon detection of general “objects” at a specified distance from the vehicle. However, since not all objects exhibit the same characteristics, particularly with respect to motion, position, and other erratic features, simply determining a distance to an unknown object at a specified time may be insufficient to avoid a collision with the autonomous vehicle if the speed or trajectory of the unknown object suddenly changes. Therefore, a computing system configured to differentiate between various types of objects facilitates complex decision making in regard to navigation and operational control of the vehicle.
Some autonomous vehicles include object classifier systems that assign labels to objects, wherein the labels are indicative of identities of the objects. An object classifier system is trained to recognize objects in sensor data (e.g., images, lidar scans, radar scans, combinations thereof, etc.) through use of labeled training data. Because the autonomous vehicle is expected to operate in a variety of conditions (e.g., at night, during rain, during foggy conditions, and so forth), a large amount of training data, across several driving contexts, is desired to accurately train the object classifier system to label a detected object.